Titlebook: Machine Learning and Knowledge Extraction; 4th IFIP TC 5, TC 12 Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2020

藥物

eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters,commonly classified over the standard taxonomy made by the World Health Organization, that is the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Particularly, classifying DiLs on the right code is crucial to allow hospitals to be refunded by Public Adminis

Resign

Mystic

Critical

The European Legal Framework for Medical AI,ability implications of AI, the Internet of Things (IoT) and robotics. In its White Paper, the Commission highlighted the “European Approach” to AI, stressing that “it is vital that European AI is grounded in our values and fundamental rights such as human dignity and privacy protection”. It also an

Overthrow

特別容易碎

Accolade

Non-local Second-Order Attention Network for Single Image Super Resolution,nvolution neural network recently are introduced into super resolution to tackle this problem and further bringing forward progress in this field. Although state-of-the-art studies have obtain excellent performance by designing the structure and the way of connection in the convolution neural networ

忍耐

ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classieters, or feature subsets. The common approach of selecting the best model using one overall metric does not necessarily find the most suitable model for a given application, since it ignores the different effects of class confusions. Expert knowledge is key to evaluate, understand and compare model

從容

Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specifincy and accuracy. However, these algorithms have recently been found to be vulnerable to adversarial examples – inputs that are crafted with the intent of causing a Deep Neural Network (DNN) to misclassify with high confidence. Although a significant amount of work has been done to find robust defen

強(qiáng)化